Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201

Sensing and mapping for interactive performance

This paper describes a trans-domain mapping (TDM) framework for translating meaningful activities from one creative domain onto another. The multi-disciplinary framework is designed to facilitate an intuitive and non-intrusive interactive multimedia performance interface that offers the users or performers real-time control of multimedia events using their physical movements. It is intended to be a highly dynamic real-time performance tool, sensing and tracking activities and changes, in order to provide interactive multimedia performances. From a straightforward definition of the TDM framework, this paper reports several implementations and multi-disciplinary collaborative projects using the proposed framework, including a motion and colour-sensitive system, a sensor-based system for triggering musical events, and a distributed multimedia server for audio mapping of a real-time face tracker, and discusses different aspects of mapping strategies in their context. Plausible future directions, developments and exploration with the proposed framework, including stage augmenta tion, virtual and augmented reality, which involve sensing and mapping of physical and non-physical changes onto multimedia control events, are discussed

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Reverse Engineering Psychologically Valid Facial Expressions of Emotion into Social Robots

Social robots are now part of human society, destined for schools, hospitals, and homes to perform a variety of tasks. To engage their human users, social robots must be equipped with the essential social skill of facial expression communication. Yet, even state-of-the-art social robots are limited in this ability because they often rely on a restricted set of facial expressions derived from theory with well-known limitations such as lacking naturalistic dynamics. With no agreed methodology to objectively engineer a broader variance of more psychologically impactful facial expressions into the social robots' repertoire, human-robot interactions remain restricted. Here, we address this generic challenge with new methodologies that can reverse-engineer dynamic facial expressions into a social robot head. Our data-driven, user-centered approach, which combines human perception with psychophysical methods, produced highly recognizable and human-like dynamic facial expressions of the six classic emotions that generally outperformed state-of-art social robot facial expressions. Our data demonstrates the feasibility of our method applied to social robotics and highlights the benefits of using a data-driven approach that puts human users as central to deriving facial expressions for social robots. We also discuss future work to reverse-engineer a wider range of socially relevant facial expressions including conversational messages (e.g., interest, confusion) and personality traits (e.g., trustworthiness, attractiveness). Together, our results highlight the key role that psychology must continue to play in the design of social robots

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery